让一篇文档同时满足多个查询需求，还能避免修改冲突。
IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimization
- 先分析不同查询的优化偏好，再融合成统一修改方案。
- 在多查询测试中显著提升性能，且跨场景更稳定。
- 适合需要兼顾多用户需求的生成式搜索系统。
随着生成式引擎通过整合检索内容直接生成答案而革新信息检索，保障来源可见性成为一大挑战。通过针对性内容修改来提升可见性是一种实用策略，称为生成式引擎优化（GEO）。然而，在有限内容预算下，为多种查询优化文档会面临约束优化难题，因异构查询常提出冲突且竞争性的修改要求。为此，我们提出IF-GEO，一种“分而后合”框架，包含两个阶段：(i) 从代表性潜在查询中挖掘差异化的优化偏好；(ii) 通过冲突感知指令融合，协调偏好以生成全局修订蓝图，指导编辑。为显式量化IF-GEO在跨查询稳定性方面的目标，我们引入风险感知稳定性度量。在多查询基准上的实验表明，IF-GEO在保持对多样化检索场景鲁棒性的同时，实现了显著的性能提升。
原文摘要 · Abstract (English)
As Generative Engines revolutionize information retrieval by synthesizing direct answers from retrieved sources, ensuring source visibility becomes a significant challenge. Improving it through targeted content revisions is a practical strategy termed Generative Engine Optimization (GEO). However, optimizing a document for diverse queries presents a constrained optimization challenge where heterogeneous queries often impose conflicting and competing revision requirements under a limited content budget. To address this challenge, we propose IF-GEO, a "diverge-then-converge" framework comprising two phases: (i) mining distinct optimization preferences from representative latent queries; (ii) synthesizing a Global Revision Blueprint for guided editing by coordinating preferences via conflict-aware instruction fusion. To explicitly quantify IF-GEO's objective of cross-query stability, we introduce risk-aware stability metrics. Experiments on multi-query benchmarks demonstrate that IF-GEO achieves substantial performance gains while maintaining robustness across diverse retrieval scenarios.
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